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23 Mar 2026

Advisor, Model Risk Management

Category:  Risk Management Division
Job Type: 
Facility:  Data & Analytics

Job Purpose

This role exists to ensure the Bank’s critical models work as intended, stand up to regulatory scrutiny, and do not create hidden risk.
Success is measured by clear validation conclusions, closed model risk issues, regulator‑ready documentation, and models that are usable, explainable, and controlled in production.

What You Will Deliver

  • Independent validation conclusions for key models across IRB, IFRS 9, Stress Testing, and AI/ML/GenAI, with clear pass/fail criteria, limitations, and required remediation.
  • A practical Model Risk Management framework that is actually applied: clear lifecycle controls, model inventory, risk tiering, approvals, and change governance.
  • Early identification and escalation of material model risks, before they become regulatory or financial issues.
  • Actionable recommendations that improve model robustness, explainability, and compliance — not academic critique.
  • Regulator‑ready evidence: validation reports, issue logs, and documentation that can be defended in audits and inspections.
  • Strong alignment between Modeling, Data, Risk, Finance, Business, and Technology, so governance does not block delivery.

Key Responsibilities

  • Independently review and challenge

           IRB models (PD, LGD, EAD) used for Basel capital

           IFRS 9 ECL models, including macro assumptions and overlays
           Stress Testing & macro models
           AI/ML/GenAI models used in material business decisions

  • Assess model risk related to data quality, assumptions, methodology, stability, performance, and explainability.
  • Own model risk issues end‑to‑end: definition, severity, remediation tracking, and closure.
  • Advise stakeholders on what must change to make models compliant, defensible, and sustainable.

Success Profile - Qualification and Experiences

Technical Knowledge

  • Strong command of international standards, with the ability to review, challenge, and conclude on real model implementations:
    • Basel II/III/IV, especially IRB mechanics (PD/LGD/EAD, downturn LGD, long‑run PD, etc.)
    • IFRS 9: ECL methodology, provisioning logic, and macroeconomic overlays
    • Model Risk Management frameworks (experience aligned with Fed SR 11‑7 is a strong advantage)
  • Solid foundation in statistics, quantitative methods, machine learning, and practical AI/GenAI applications in modeling.
  • Ability to assess model risk in practice: data suitability, robustness, stability, and explainability—with clear evidence and implications.

Skills

  • Strong analytical thinking with the ability to frame conclusions through a risk, strategy, and regulatory lens.
  • Clear, credible communication—able to advise senior management and work effectively across multiple functions.
  • Proficient in at least one of Python / SAS / R / SQL; experience with ML/AI libraries is a plus (e.g., scikit‑learn, XGBoost, SHAP, LIME).

Experience

  • Minimum 10 years in risk modeling, model risk management, or independent model validation.
  • Hands‑on experience delivering or validating IRB, IFRS 9, and/or AI/GenAI models in a banking environment is a significant advantage.

Apply now »